In the first quarter of 2025, almost two-thirds percent of the total wealth in the United States was owned by the top 10 percent of earners. In comparison, the lowest 50 percent of earners only owned 2.5 percent of the total wealth. Income inequality in the U.S. Despite the idea that the United States is a country where hard work and pulling yourself up by your bootstraps will inevitably lead to success, this is often not the case. In 2023, 7.4 percent of U.S. households had an annual income under 15,000 U.S. dollars. With such a small percentage of people in the United States owning such a vast majority of the country’s wealth, the gap between the rich and poor in America remains stark. The top one percent The United States was the country with the most billionaires in the world in 2025. Elon Musk, with a net worth of 342 billion U.S. dollars, was among the richest people in the United States in 2025. Over the past 50 years, the CEO-to-worker compensation ratio has exploded, causing the gap between rich and poor to grow, with some economists theorizing that this gap is the largest it has been since right before the Great Depression.

Graph and download economic data for Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBLT01026) from Q3 1989 to Q1 2025 about net worth, wealth, percentile, Net, and USA.

In the third quarter of 2024, the top ten percent of earners in the United States held over ** percent of total wealth. This is fairly consistent with the second quarter of 2024. Comparatively, the wealth of the bottom ** percent of earners has been slowly increasing since the start of the *****, though remains low. Wealth distribution in the United States by generation can be found here.

In 2023, just over 50 percent of Americans had an annual household income that was less than 75,000 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023. Income and wealth in the United States After the economic recession in 2009, income inequality in the U.S. is more prominent across many metropolitan areas. The Northeast region is regarded as one of the wealthiest in the country. Maryland, New Jersey, and Massachusetts were among the states with the highest median household income in 2020. In terms of income by race and ethnicity, the average income of Asian households was 94,903 U.S. dollars in 2020, while the median income for Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates its list of poverty levels. Preliminary estimates show that the average poverty threshold for a family of four people was 26,500 U.S. dollars in 2021, which is around 100 U.S. dollars less than the previous year. There were an estimated 37.9 million people in poverty across the United States in 2021, which was around 11.6 percent of the population. Approximately 19.5 percent of those in poverty were Black, while 8.2 percent were white.

Over ** million individuals residing in the United States belonged to the global top one percent of ultra-high net worth individuals worldwide in 2022. China ranked second, with over **** million top one percent wealth holders globally. France followed in third.

In 2023, the percentage of income held by the richest 20 percent of the population in Colombia amounted to 58.7 percent. Between 1980 and 2023, the figure dropped by 0.3 percentage points, though the decline followed an uneven course rather than a steady trajectory.

Around ** percent of the world's collected net personal wealth belongs to the richest one percent. The share of global wealth owned by the richest percent fell during the global financial crisis in 2008/2009, and has been fluctuating since. One-third of the world's billionaires reside in North America.

Data and insights on Wealth Distribution in India - share of wealth, average wealth, HNIs, wealth inequality GINI, and comparison with global peers.

Ratio of household equivalised income of the top 10 per cent of households to the income of the bottom 10 per cent of households.

Ratio calculated using weekly household income adjusted to take account of differences in numbers and ages of residents.

This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.

This dataset is one of the Greater London Authority's measures of Economic Development strategy. Click here to find out more.

Our aim is to provide an overview of associations between income inequality and intergenerational mobility in the United States, Canada, and eight European countries (Denmark, Finland, France, Germany, Italy, Norway, Sweden, and the United Kingdom). We analyze whether this correlation is observed across and within countries over time. Developed countries have been experiencing increases in inequality in recent decades, mostly due to a steep concentration of income at the top of the distribution. We investigate Great Gatsby curves and perform meta-regression analyses based upon several papers on this topic. Results suggest that countries with high levels of inequality tend to have lower levels of mobility. Intergenerational income elasticities have stronger associations with the Gini coefficient, compared to associations with the top one percent income share. Once models are controlled for methodological variables, country indicators, and paper indicators, correlations of mobility with the Gini coefficient lose significance, but not with the top one percent income share. This result is an indication that recent increases in inequality at the top of the distribution (captured by the top one percent income share) might be negatively affecting mobility on a greater magnitude, compared to variations across the income distribution (captured by the Gini coefficient).

As of 2023, the countries in Europe with the greatest share of national wealth taken by the top one percent of wealthy people were Russia, Turkey, and Hungary, with over two-thirds of wealth in Russia being owned by the wealthiest decile. On the other hand, the Netherlands, Belgium, and Slovakia were the countries with the smallest share of national wealth going to the top one percent, with more than half of wealth in the Netherlands going to the bottom 90 percent.

Goal 10Reduce inequality within and among countriesTarget 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national averageIndicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total populationSI_HEI_TOTL: Growth rates of household expenditure or income per capita (%)Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other statusIndicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilitiesSI_POV_50MI: Proportion of people living below 50 percent of median income (%)Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regardIndicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights lawVC_VOV_GDSD: Proportion of population reporting having felt discriminated against, by grounds of discrimination, sex and disability (%)Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equalityIndicator 10.4.1: Labour share of GDPSL_EMP_GTOTL: Labour share of GDP (%)Indicator 10.4.2: Redistributive impact of fiscal policySI_DST_FISP: Redistributive impact of fiscal policy, Gini index (%)Target 10.5: Improve the regulation and monitoring of global financial markets and institutions and strengthen the implementation of such regulationsIndicator 10.5.1: Financial Soundness IndicatorsFI_FSI_FSANL: Non-performing loans to total gross loans (%)FI_FSI_FSERA: Return on assets (%)FI_FSI_FSKA: Regulatory capital to assets (%)FI_FSI_FSKNL: Non-performing loans net of provisions to capital (%)FI_FSI_FSKRTC: Regulatory Tier 1 capital to risk-weighted assets (%)FI_FSI_FSLS: Liquid assets to short term liabilities (%)FI_FSI_FSSNO: Net open position in foreign exchange to capital (%)Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutionsIndicator 10.6.1: Proportion of members and voting rights of developing countries in international organizationsSG_INT_MBRDEV: Proportion of members of developing countries in international organizations, by organization (%)SG_INT_VRTDEV: Proportion of voting rights of developing countries in international organizations, by organization (%)Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policiesIndicator 10.7.1: Recruitment cost borne by employee as a proportion of monthly income earned in country of destinationIndicator 10.7.2: Number of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of peopleSG_CPA_MIGRP: Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%)SG_CPA_MIGRS: Countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets)Indicator 10.7.3: Number of people who died or disappeared in the process of migration towards an international destinationiSM_DTH_MIGR: Total deaths and disappearances recorded during migration (number)Indicator 10.7.4: Proportion of the population who are refugees, by country of originSM_POP_REFG_OR: Number of refugees per 100,000 population, by country of origin (per 100,000 population)Target 10.a: Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreementsIndicator 10.a.1: Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariffTM_TRF_ZERO: Proportion of tariff lines applied to imports with zero-tariff (%)Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmesIndicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)DC_TRF_TOTDL: Total assistance for development, by donor countries (millions of current United States dollars)DC_TRF_TOTL: Total assistance for development, by recipient countries (millions of current United States dollars)DC_TRF_TFDV: Total resource flows for development, by recipient and donor countries (millions of current United States dollars)Target 10.c: By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per centIndicator 10.c.1: Remittance costs as a proportion of the amount remittedSI_RMT_COST: Remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_BC: Corridor remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_SC: SmaRT corridor remittance costs as a proportion of the amount remitted (%)

This study examines the effect of inflation targeting (IT) on income distribution in a panel of 70 countries. Employing a variety of propensity score matching methods, we find strong evidence that that incomes became more unequal in IT-adopting countries relative to countries that did not adopt IT. On average, IT has been associated with a relative rise in the pre- and post-tax Gini coefficients of about 2 percentage points, and a relative increase in the share of national income going to the top 1% and 10% of households by about 12 percentage points and 13-17 percentage points.

Using Danish administrative data, we estimate the impact of bequests on the level and inequality of wealth. We compare the distributions of wealth over time of people whose parent died and those whose parent did not. Bequests account for 26 percent of the average post-bequest wealth 1-3 years after parental death and significantly affect wealth throughout the distribution. Bequests increase absolute wealth inequality (variance of the distribution censored at the top/bottom 1% increases by 33 percent), but reduce relative inequality (the top 1% share declines by 6 percentage points from the base of 31 percent).

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Florence, SC, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 10,346, while the mean income for the highest quintile (20% of households with the highest income) is 208,770. This indicates that the top earners earn 20 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 368,659, which is 176.59% higher compared to the highest quintile, and 3563.30% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Florence median household income. You can refer the same here

Taking five Anglo-Saxon countries that have relatively similar backgrounds and tax systems – Australia, Canada, New Zealand, the UK, and the US – we see that the shares of the very richest exhibit a strikingly similar pattern, falling in the three decades after World War II, before rising sharply from the mid-1970s onwards. The share of the top 1 percent is highly correlated across Anglo-Saxon countries, more so than with the share of the next 4 percent. Controlling for country and year fixed effects, we find that a reduction in the marginal tax rate on wage income is associated with an increase in the share of the top percentile group. Likewise, a fall in the marginal tax rate on investment income (based on a lagged moving average) is associated with a rise in the share of the top percentile group.

The wealth Gini index value varies between 0 (perfect equality, i.e. all households or individuals have the same wealth) and 1 (perfect inequality, i.e. one household or individual owns all the wealth, the others have none). The wealth share of the top 10% is the share of wealth owned by the richest 10% of the wealth distribution.

The share of total national wealth owned by the top one percent of wealthy people in most major European economies rose over the period from 1995 to 2023. The growth from 21.5 percent in 1995 to 48.6 percent share in Russia is particularly striking, as the poweful 'oligarchs' at the top of Russian society increased their share of that country's national wealth from less than a fifth in 1995, to almost half in 2023.

This article examines the relationship between income concentration and policy outputs that determine the generosity of two major state-level safety net programs: unemployment insurance and cash social assistance. Using a difference in differences framework, it tests the degree to which the top 1 percent share is associated with benefit replacement rates for these programs during the period 1978–2010. The results suggest that higher state income inequality lowers those states’ welfare benefits significantly in ways consistent with a “plutocracy” hypothesis that has been suggested in work by scholars such as Bartels, Bonica, Gilens, and Page. The results are robust to controls for several alternative explanations for benefit generosity, including citizen ideology, party control of government, fiscal pressure on programs, state racial heterogeneity, and public opinion liberalism. The results thus support the notion that growing income concentration at the very top undermines social protection policies.